Cutting Edge Case Solution
Question: 2c.
The five days’ moving average method has the specialty of using multiple data points and the recent history to forecast the new value. It is explained as the average of data for the n period of time and Harry used n as 5,which means that the data of last 5 days was taken for forecasting. Further, it is also argued against the method that this method may ignore the changing environment because it allocates same weight to all the selected values. And in the accuracy chart; moving average is listed at third,with MAD value of 218 as compared to the other forecasting methods.
Question: 2d.
As exponential smoothing method is bit same as moving average method, the only change is that it gives weight to the recent observation and allocates a smaller weight to the next progression towards the older data point. It has alpha in its formula and this alpha defines the stability of the process.Whereas,Harry used 0.1 alpha because he stated that the conditions will remain stable and he will get the MAD of 249, and it is ranked forth according to its data accuracy.
Question: 2e.
Increasing the alpha value represents that the recent vales are given higher weights as compared to previous values in the time series data set. The variability in the output is high, because the response towards the conditions is fast, which creates high variability in the results, and in the accuracy table; this method is ranked on first position.
Question: 2f.
After analyzing the aforementioned forecast methods; we can clearly see that if MAD is low then it is accurate forecasting. So, we can say that the mean values should not deviate from the actual,and that is to reduce the MAD value.
Question: 3a.
s.no | Year | Month | Volume | Forecasted |
1 | 2014 | Jan | 24,015 | |
2 | 2014 | Feb | 25,203 | 24,015 |
3 | 2014 | Mar | 23,589 | 24,609 |
4 | 2014 | Apr | 26,704 | 24,099 |
5 | 2014 | May | 28,120 | 25,402 |
6 | 2014 | Jun | 26,321 | 26,761 |
7 | 2014 | Jul | 27,021 | 26,541 |
8 | 2014 | Aug | 25,981 | 26,781 |
9 | 2014 | Sep | 26,456 | 26,381 |
10 | 2014 | Oct | 27,120 | 26,418 |
11 | 2014 | Nov | 26,954 | 26,769 |
12 | 2014 | Dec | 27,321 | 26,862 |
13 | 2015 | Jan | 26,456 | 27,091 |
14 | 2015 | Feb | 27,450 | 26,774 |
15 | 2015 | Mar | 31,580 | 27,112 |
16 | 2015 | Apr | 33,124 | 29,346 |
17 | 2015 | May | 32,432 | 31,235 |
18 | 2015 | Jun | 31,901 | 31,833 |
19 | 2015 | July | 31,867 |
Call Volume Forecast for July 2015 exponential smoothing is 31867.
Question: 3b.
Year | Month | Volume | Count | Forecast |
2014 | Jan | 24,015 | 62,120 | 23967.53 |
2014 | Feb | 25,203 | 62,152 | 24401.06 |
2014 | Mar | 23,589 | 62,138 | 24834.59 |
2014 | Apr | 26,704 | 70,343 | 25268.12 |
2014 | May | 28,120 | 69,120 | 25701.65 |
2014 | Jun | 26,321 | 68,967 | 26135.18 |
2014 | Jul | 27,021 | 67,956 | 26568.71 |
2014 | Aug | 25,981 | 65,342 | 27002.24 |
2014 | Sep | 26,456 | 65,380 | 27435.77 |
2014 | Oct | 27,120 | 65,432 | 27869.3 |
2014 | Nov | 26,954 | 65,423 | 28302.83 |
2014 | Dec | 27,321 | 65,650 | 28736.36 |
2015 | Jan | 26,456 | 65,620 | 29169.89 |
2015 | Feb | 27,450 | 65,610 | 29603.42 |
2015 | Mar | 31,580 | 77,231 | 30036.95 |
2015 | Apr | 33,124 | 75,201 | 30470.48 |
2015 | May | 32,432 | 74,978 | 30904.01 |
2015 | Jun | 31,901 | 75,012 | 31337.54 |
2015 | July | 31,771 |
The forecast for July 2015 is 31771.
Question: 3c.
s.no | Year | Month | Volume | reg forecast | Forecasted | MAD | Ab. Error | Ab. Error |
1 | 2014 | Jan | 24,015 | 23,968 | 24000 | 15 | 47 | 47.47 |
2 | 2014 | Feb | 25,203 | 24,401 | 24,015 | 1,188 | 802 | 801.94 |
3 | 2014 | Mar | 23,589 | 24,835 | 24,609 | -1,020 | -1,246 | 1245.59 |
4 | 2014 | Apr | 26,704 | 25,268 | 24,099 | 2,605 | 1,436 | 1435.88 |
5 | 2014 | May | 28,120 | 25,702 | 25,402 | 2,719 | 2,418 | 2418.35 |
6 | 2014 | Jun | 26,321 | 26,135 | 26,761 | -440 | 186 | 185.82 |
7 | 2014 | Jul | 27,021 | 26,569 | 26,541 | 480 | 452 | 452.29 |
8 | 2014 | Aug | 25,981 | 27,002 | 26,781 | -800 | -1,021 | 1021.24 |
9 | 2014 | Sep | 26,456 | 27,436 | 26,381 | 75 | -980 | 979.77 |
10 | 2014 | Oct | 27,120 | 27,869 | 26,418 | 702 | -749 | 749.3 |
11 | 2014 | Nov | 26,954 | 28,303 | 26,769 | 185 | -1,349 | 1348.83 |
12 | 2014 | Dec | 27,321 | 28,736 | 26,862 | 459 | -1,415 | 1415.36 |
13 | 2015 | Jan | 26,456 | 29,170 | 27,091 | -635 | -2,714 | 2713.89 |
14 | 2015 | Feb | 27,450 | 29,603 | 26,774 | 676 | -2,153 | 2153.42 |
15 | 2015 | Mar | 31,580 | 30,037 | 27,112 | 4,468 | 1,543 | 1543.05 |
16 | 2015 | Apr | 33,124 | 30,470 | 29,346 | 3,778 | 2,654 | 2653.52 |
17 | 2015 | May | 32,432 | 30,904 | 31,235 | 1,197 | 1,528 | 1527.99 |
18 | 2015 | Jun | 31,901 | 31,338 | 31,833 | 68 | 563 | 563.46 |
19 | 2015 | July | 31,867 | 873 | Abs. error | 1292.065 |
The mean absolute deviation of exponential smoothing model is 873 when alpha is 0.5 and absolute error is 1292.065 so the difference between both values is due to the user counts in both data. As this take count and the previous do not take any count..............................
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